Ed

Edit images in the browser using GPT-3 and WebAssembly

Hacker News

Edit images in the browser using GPT-3 and WebAssembly

Hi HN! A little while ago I had to do an image edit that was fairly simple but too complex for Preview. As someone who isn't great at Photoshop, I turned to ImageMagick. But then I had to figure out the correct ImageMagick command and get ImageMagick installed on my laptop. In total, a simple image edit took about 30 minutes. I made ImageCalc to make that easier. It uses GPT-3 to turn a plain text description into an ImageMagick command, and then WASM[1] to run ImageMagick in the browser (so your image never leaves your device). Here's a quick demo of what it can do: https://www.youtube.com/watch?v=0u3sXqgh3A8 It doesn't work 100% of the time, but it's so quick to try that I've already found myself reaching for ImageCalc before anything else. It's especially helpful if you want to figure out the right command for a web service or batch command. Hope you find it useful! https://imagecalc.com [1] https://github.com/KnicKnic/WASM-ImageMagick

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Product HuntOn track for Day 1 leaderboard · Strong signals: using, plain · Missing: mac, agents, macos
77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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